Authors:
Yasuaki Kuroe
1
;
Hitoshi Iima
2
and
Yutaka Maeda
3
Affiliations:
1
Faculty of Engineering Science, Kansai University, Suita-shi, Osaka, Japan, Faculty of Information and Human Sciences, Kyoto Institute of Technology, Kyoto and Japan
;
2
Faculty of Information and Human Sciences, Kyoto Institute of Technology, Kyoto and Japan
;
3
Faculty of Engineering Science, Kansai University, Suita-shi, Osaka and Japan
Keyword(s):
Spiking Neural Network, Learning Method, Particle Swarm Optimization, Burst Firing, Periodic Firing.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Complex Artificial Neural Network Based Systems and Dynamics
;
Computational Intelligence
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Learning Paradigms and Algorithms
;
Methodologies and Methods
;
Neural Networks
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Signal Processing
;
Soft Computing
;
Theory and Methods
Abstract:
Recently it has been reported that artificial spiking neural networks (SNNs) are computationally more powerful than the conventional neural networks. In biological neural networks of living organisms, various firing patterns of nerve cells have been observed, typical examples of which are burst firings and periodic firings. In this paper we propose a learning method which can realize various firing patterns for recurrent SNNs (RSSNs). We have already proposed learning methods of RSNNs in which the learning problem is formulated such that the number of spikes emitted by a neuron and their firing instants coincide with given desired ones. In this paper, in addition to that, we consider several desired properties of a target RSNN and proposes cost functions for realizing them. Since the proposed cost functions are not differentiable with respect to the learning parameters, we propose a learning method based on the particle swarm optimization.